Data Pipelines and Feature Platforms Questions

Designing and operating data pipelines and feature platforms involves engineering reliable, scalable systems that convert raw data into production ready features and deliver those features to both training and inference environments. Candidates should be able to discuss batch and streaming ingestion architectures, distributed processing approaches using systems such as Apache Spark and streaming engines, and orchestration patterns using workflow engines. Core topics include schema management and evolution, data validation and data quality monitoring, handling event time semantics and operational challenges such as late arriving data and data skew, stateful stream processing, windowing and watermarking, and strategies for idempotent and fault tolerant processing. The role of feature stores and feature platforms includes feature definition management, feature versioning, point in time correctness, consistency between training and serving, online low latency feature retrieval, offline materialization and backfilling, and trade offs between real time and offline computation. Feature engineering strategies, detection and mitigation of distribution shift, dataset versioning, metadata and discoverability, governance and compliance, and lineage and reproducibility are important areas. For senior and staff level candidates, design considerations expand to multi tenant platform architecture, platform application programming interfaces and onboarding, access control, resource management and cost optimization, scaling and partitioning strategies, caching and hot key mitigation, monitoring and observability including service level objectives, testing and continuous integration and continuous delivery for data pipelines, and operational practices for supporting hundreds of models across teams.

HardSystem Design

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You must support exactly-once semantics end-to-end from Kafka ingestion through to an online feature store. Explain an architecture using available technologies (e.g., Kafka transactions, Flink checkpoints, idempotent sink writes) and the guarantees each component provides.

EasyTechnical

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What is event time vs processing time in streaming systems? Give an example where using processing time would produce incorrect features, and explain how you would correct for it.

MediumTechnical

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A model consumes features from both an offline dataset and an online store. Describe an approach to verify training-serving consistency and detect regressions caused by differences between offline and online feature values.

EasySystem Design

0 practiced

Describe the end-to-end architecture you would use to build a simple batch feature pipeline that produces training datasets from raw event logs stored in S3. Include components for ingestion, schema management, validation, transformation, feature storage, and how you would ensure point-in-time correctness for training data.

HardTechnical

0 practiced

A stakeholder asks for feature importance for a production model to explain predictions. Discuss how you would compute and expose feature attributions in an environment where features are materialized from complex pipelines (some online, some offline). Consider reproducibility and explainability constraints.

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